label alignment using HVite is added.

acoustic_model/acoustic_model.pyproj

@@ -4,7 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>fame_hmm.py</StartupFile>
+    <StartupFile>htk_vs_kaldi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>

acoustic_model/convert_phoneset.py

@@ -38,3 +38,9 @@ def convert_phoneset(word_list, translation_key):
 		translation_key (dict): 
 	"""
 	return [translation_key.get(phone, phone) for phone in word_list]
+
+
+def phone_reduction(phones, reduction_key):
+	multi_character_tokenize(wo.strip(), multi_character_phones)
+	return [reduction_key.get(i, i) for i in phones
+				  if not i in phones_to_be_removed]

acoustic_model/defaultfiles.py

@@ -17,6 +17,7 @@ novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
 rug_dir = r'c:\OneDrive\Research\rug'
 experiments_dir = os.path.join(rug_dir, 'experiments')
 htk_dir		= os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
+kaldi_dir   = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', '_stimmen')
 stimmen_dir = os.path.join(experiments_dir, 'stimmen')
 
 # data

acoustic_model/fame_functions.py

@@ -321,9 +321,11 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
 	lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
 
 
-def fix_single_quote(lexicon_file):
-	""" add '\' before all single quote at the beginning of words.
-	convert special characters to ascii compatible characters.
+def fix_lexicon(lexicon_file):
+	""" fix lexicon 
+	- add '\' before all single quote at the beginning of words.
+	- convert special characters to ascii compatible characters.
+	- add silence.
 
 	Args:
 		lexicon_file (path): lexicon file, which will be overwitten.
@@ -331,6 +333,12 @@ def fix_single_quote(lexicon_file):
 	"""
 	lex = load_lexicon(lexicon_file)
 	lex = lex.dropna() # remove N/A.
+
+	# add 'sil'
+	row = pd.Series(['SILENCE', 'sil'], index=lex.columns)
+	lex = lex.append(row, ignore_index=True)
+	lex = lex.sort_values(by='word', ascending=True)
+
 	for i in lex[lex['word'].str.startswith('\'')].index.values:
 		lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
 	# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
@@ -346,10 +354,11 @@ def word2htk(word):
 def ipa2asr(ipa):
 	curr_dir = os.path.dirname(os.path.abspath(__file__))
 	translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
-
+	#ipa_ = fame_asr.phone_reduction(ipa)
 	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
 	ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
 	asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
+	asr_splitted = fame_asr.phone_reduction(asr_splitted)
 	return ''.join(asr_splitted)
 
 
@@ -360,5 +369,6 @@ def ipa2htk(ipa):
 	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
 	ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
 	asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
+	asr_splitted = fame_asr.phone_reduction(asr_splitted)
 	htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
 	return ''.join(htk_splitted)

acoustic_model/fame_hmm.py

@@ -27,7 +27,8 @@ extract_features  = 0
 flat_start		  = 0
 train_model_without_sp = 0
 add_sp = 0
-train_model_with_sp    = 1
+train_model_with_sp    = 0
+train_model_with_sp_align_mlf = 1
 
 
 
@@ -75,6 +76,7 @@ if not os.path.exists(label_dir):
 ## training
 hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
 mlf_file_train   = os.path.join(label_dir, 'train_phone.mlf')
+mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
 
 ## train without sp
 niter_max = 10
@@ -102,7 +104,8 @@ if make_lexicon:
 	# (1) Replace all tabs with single space;
 	# (2) Put a '\' before any dictionary entry beginning with single quote 
 	#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
-	fame_functions.fix_single_quote(lexicon_htk)
+	print('>>> fixing the lexicon...')
+	fame_functions.fix_lexicon(lexicon_htk)
 	print("elapsed time: {}".format(time.time() - timer_start))
 
 
@@ -269,11 +272,11 @@ if train_model_without_sp:
 		fh.make_new_directory(modeln_dir)
 		pyhtk.re_estimation(
 			config_train,
-			os.path.join(modeln_dir_pre, 'macros'),
 			os.path.join(modeln_dir_pre, hmmdefs_name), 
 			modeln_dir,
 			hcompv_scp_train, phonelist_txt,
-			mlf_file=mlf_file_train)
+			mlf_file=mlf_file_train, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
 		print("elapsed time: {}".format(time.time() - timer_start))
 
 
@@ -321,7 +324,6 @@ if add_sp:
 ## ======================= train model with short pause =======================
 if train_model_with_sp:
 	print('==== train model with sp ====')
-	#for niter in range(niter_max+1, niter_max*2+1):
 	for niter in range(20, 50):
 		timer_start = time.time()
 		hmm_n = 'iter' + str(niter)
@@ -333,9 +335,31 @@ if train_model_with_sp:
 		fh.make_new_directory(modeln_dir)
 		pyhtk.re_estimation(
 			config_train,
-			os.path.join(modeln_dir_pre, 'macros'),
 			os.path.join(modeln_dir_pre, hmmdefs_name), 
 			modeln_dir,
 			hcompv_scp_train, phonelist_txt,
-			mlf_file=mlf_file_train)
+			mlf_file=mlf_file_train, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
+		print("elapsed time: {}".format(time.time() - timer_start))
+		
+
+## ======================= train model with short pause =======================
+if train_model_with_sp_align_mlf:
+	print('==== train model with sp with align.mlf ====')
+	for niter in range(50, 60):
+		timer_start = time.time()
+		hmm_n = 'iter' + str(niter)
+		hmm_n_pre = 'iter' + str(niter-1)
+		modeln_dir	   = os.path.join(model1_dir, hmm_n)
+		modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre) 
+		
+		# re-estimation
+		fh.make_new_directory(modeln_dir)
+		pyhtk.re_estimation(
+			config_train,
+			os.path.join(modeln_dir_pre, hmmdefs_name), 
+			modeln_dir,
+			hcompv_scp_train, phonelist_txt,
+			mlf_file=mlf_file_train_aligned, 
+			macros=os.path.join(modeln_dir_pre, 'macros'))
 		print("elapsed time: {}".format(time.time() - timer_start))

acoustic_model/htk_vs_kaldi.py

@@ -11,6 +11,7 @@ import glob
 
 import numpy as np
 import pandas as pd
+from collections import Counter
 #import matplotlib.pyplot as plt
 #from sklearn.metrics import confusion_matrix
 
@@ -50,11 +51,14 @@ from htk import pyhtk
 #lex_asr	 = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
 #lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
 
-## procedure
+# procedure
+make_dic_file = 0
+make_HTK_files = 1
+extract_features = 0
 #make_htk_dict_files = 0
 #do_forced_alignment_htk = 0
 #eval_forced_alignment_htk = 0
-#make_kaldi_data_files = 0
+make_kaldi_files = 0
 #make_kaldi_lexicon_txt = 0
 #load_forced_alignment_kaldi = 1
 #eval_forced_alignment_kaldi = 1
@@ -66,13 +70,34 @@ from htk import pyhtk
 #sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
 #from evaluation import plot_confusion_matrix
 
-config_dir = os.path.join(default.htk_dir, 'config')
-model_dir = os.path.join(default.htk_dir, 'model')
-lattice_file = os.path.join(config_dir, 'stimmen.ltc')
-#pyhtk.create_word_lattice_file(
-#	os.path.join(config_dir, 'stimmen.net'), 
-#	lattice_file)
-hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
+## HTK related files.
+config_dir	 = os.path.join(default.htk_dir, 'config')
+model_dir	 = os.path.join(default.htk_dir, 'model')
+feature_dir  = os.path.join(default.htk_dir, 'mfc', 'stimmen')
+
+config_hcopy = os.path.join(config_dir, 'config.HCopy')
+
+# files to be made.
+lattice_file  = os.path.join(config_dir, 'stimmen.ltc')
+phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
+stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
+hcopy_scp   = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
+hvite_scp   = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp')
+hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp')
+
+
+## Kaldi related files.
+kaldi_data_dir = os.path.join(default.kaldi_dir, 'data')
+
+# files to be made.
+wav_scp    = os.path.join(kaldi_data_dir, 'test', 'wav.scp')
+text_file  = os.path.join(kaldi_data_dir, 'test', 'text')
+utt2spk    = os.path.join(kaldi_data_dir, 'test', 'utt2spk')
+corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt')
+lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt')
+nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt')
+silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt')
+optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt')
 
 
 ## ======================= load test data ======================
@@ -85,392 +110,468 @@ df = stimmen_functions.add_row_htk(df)
 word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
 word_list = sorted(word_list)
 
-# pronunciation variants
+
+## ======================= make dic file to check pronunciation variants ======================
+# dic file should be manually modified depends on the task - recognition / forced-alignemnt.
+if make_dic_file:
+	# for HTK.
+	with open(stimmen_dic, mode='wb') as f:
+		for word in word_list:
+			df_ = df[df['word']==word]
+			pronunciations = list(np.unique(df_['htk']))
+			pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word(
+				htk, fame_asr.multi_character_phones_htk)) + ' sil'
+				for htk in pronunciations]
+			f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii'))
+			f.write(bytes('SILENCE sil\n', 'ascii'))
+
+	# for Kaldi.
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict'))
+	with open(lexicon_txt, mode='wb') as f:
+		f.write(bytes('!SIL sil\n', 'utf-8'))		
+		f.write(bytes('<UNK> spn\n', 'utf-8'))
+		for word in word_list:
+			df_ = df[df['word']==word]
+			pronunciations = list(np.unique(df_['asr']))
+			pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word(
+				asr, fame_asr.multi_character_phones))
+				for asr in pronunciations]
+			f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8'))
+
+
+## ======================= test data for recognition ======================
+# only target pronunciation variants.
+df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
 for word in word_list:
-	df_ = df[df['word']==word]
-	print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
+	variants = [htk.replace(' ', '') 
+				   for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
+	df_ = df[df['word'] == word]
+	for index, row in df_.iterrows():
+		if row['htk'] in variants:
+			df_rec = df_rec.append(row, ignore_index=True)
 
-#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
 
-#output = pyhtk.recognition(
-#	os.path.join(default.htk_dir, 'config', 'config.rec', 
-#	lattice_file, 
-#	os.path.join(model_dir, 'hmm1', 'iter13'),
-#	dictionary_file, 
-#	os.path.join(config_dir, 'phonelist.txt'), 
-#	hvite_scp)
+## ======================= make files required for HTK ======================
+if make_HTK_files:
+	# make a word lattice file.
+	pyhtk.create_word_lattice_file(
+		os.path.join(config_dir, 'stimmen.net'), 
+		lattice_file)
 
-			#pyhtk.create_label_file(
-			#	row['word'], 
-			#	os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
+	# extract features.
+	with open(hcopy_scp, 'wb') as f:
+		filelist = [os.path.join(stimmen_test_dir, filename) + '\t' 
+	+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc')) 
+				for filename in df['filename']]
+		f.write(bytes('\n'.join(filelist), 'ascii'))
+	pyhtk.wav2mfc(config_hcopy, hcopy_scp)
 
-## ======================= make a HTK dic file ======================
-#if make_htk_dic_file:
-#    output_type = 3
-dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic') 
-#for word in word_list:
-word = word_list[2]
-# pronunciation variant of the target word.
-pronunciations = df_test['asr'][df_test['word'].str.match(word)]
+	# make label files.
+	for index, row in df.iterrows():
+		filename = row['filename'].replace('.wav', '.lab')
+		label_file = os.path.join(feature_dir, filename)
+		with open(label_file, 'wb') as f:
+			label_string = 'START\n' + row['word'].upper() + '\nEND\n'
+			f.write(bytes(label_string, 'ascii'))
+
+
+## ======================= make files required for Kaldi =======================
+if make_kaldi_files:
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'test'))
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local'))
+	fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf'))
+	
+	# remove previous files.
+	if os.path.exists(wav_scp):
+		os.remove(wav_scp)
+	if os.path.exists(text_file):
+		os.remove(text_file)
+	if os.path.exists(utt2spk):
+		os.remove(utt2spk)
+
+	f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
+	f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
+	f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
+
+	# make wav.scp, text, and utt2spk files.
+	for i, row in df_rec.iterrows():
+		filename = row['filename']
+		print('=== {0}: {1} ==='.format(i, filename))
+
+		wav_file = os.path.join(stimmen_test_dir, filename)
+		#if os.path.exists(wav_file):
+		speaker_id = 'speaker_' + str(i).zfill(4)
+		utterance_id = filename.replace('.wav', '')
+		utterance_id = utterance_id.replace(' ', '_')
+		utterance_id = speaker_id + '-' + utterance_id
+
+		# output
+		f_wav_scp.write('{0} {1}\n'.format(
+			utterance_id, 
+			wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format.
+		f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower()))
+		f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
+
+	f_wav_scp.close()
+	f_text_file.close()
+	f_utt2spk.close()
+
+	with open(corpus_txt, 'wb') as f:
+		f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8'))
+
+	with open(nonsilence_phones_txt, 'wb') as f:
+		f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8'))
+
+	with open(silence_phones_txt, 'wb') as f:
+		f.write(bytes('sil\nspn\n', 'utf-8'))
+
+	with open(optional_silence_txt, 'wb') as f:
+		f.write(bytes('sil\n', 'utf-8'))
+
+	with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f:
+		f.write(bytes('first_beam=10.0\n', 'utf-8'))
+		f.write(bytes('beam=13.0\n', 'utf-8'))
+		f.write(bytes('lattice_beam=6.0\n', 'utf-8'))
+
+	with open(os.path.join(kaldi_data_dir, 'conf', 'mfcc.conf'), 'wb') as f:
+		f.write(bytes('--use-energy=false', 'utf-8'))
+
+
+## ======================= recognition ======================
+
+listdir = glob.glob(os.path.join(feature_dir, '*.mfc'))
+with open(hvite_scp, 'wb') as f:
+	f.write(bytes('\n'.join(listdir), 'ascii'))
+
+with open(hresult_scp, 'wb') as f:
+	f.write(bytes('\n'.join(listdir).replace('.mfc', '.rec'), 'ascii'))
+
+
+# calculate result
+performance = np.zeros((1, 2))
+for niter in range(1, 50):
+	output = pyhtk.recognition(
+		os.path.join(config_dir, 'config.rec'),
+		lattice_file,
+		os.path.join(default.htk_dir, 'model', 'hmm1', 'iter' + str(niter), 'hmmdefs'),
+		stimmen_dic, phonelist_txt, hvite_scp)
+
+	output = pyhtk.calc_recognition_performance(
+		stimmen_dic, hresult_scp)
+	per_sentence, per_word = pyhtk.load_recognition_output_all(output)
+	performance_ = np.array([niter, per_sentence['accuracy']]).reshape(1, 2)
+	performance = np.r_[performance, performance_]
+	print('{0}: {1}[%]'.format(niter, per_sentence['accuracy']))
 
-        # make dic file.
-        #am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
 
 
 ## ======================= forced alignment using HTK =======================
 if do_forced_alignment_htk:
 	
-    #for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-    for hmm_num in [256, 512, 1024]:
-        hmm_num_str = str(hmm_num)
-        acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
+	#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+	for hmm_num in [256, 512, 1024]:
+		hmm_num_str = str(hmm_num)
+		acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
 
-        predictions = pd.DataFrame({'filename': [''], 
-                    'word': [''], 
-                    'xsampa': [''],
-                    'ipa': [''],
-                    'famehtk': [''],
-                    'prediction': ['']})
-        for i, filename in enumerate(df['filename']):
-            print('=== {0}/{1} ==='.format(i, len(df)))
-            if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
-                wav_file = os.path.join(wav_dir, filename)
-                if os.path.exists(wav_file):
-                    word = df['word'][i]
-                    WORD = word.upper()
-                    fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
+		predictions = pd.DataFrame({'filename': [''], 
+					'word': [''], 
+					'xsampa': [''],
+					'ipa': [''],
+					'famehtk': [''],
+					'prediction': ['']})
+		for i, filename in enumerate(df['filename']):
+			print('=== {0}/{1} ==='.format(i, len(df)))
+			if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
+				wav_file = os.path.join(wav_dir, filename)
+				if os.path.exists(wav_file):
+					word = df['word'][i]
+					WORD = word.upper()
+					fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
 					
-                    #if not os.path.exists(fa_file):
-                    # make label file.
-                    label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
-                    with open(label_file, 'w') as f:
-                        lines = f.write(WORD)
+					#if not os.path.exists(fa_file):
+					# make label file.
+					label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
+					with open(label_file, 'w') as f:
+						lines = f.write(WORD)
 
-                    htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+					htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
 
-                    pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, 
-                                default.phonelist, acoustic_model)
-                    os.remove(label_file)
+					pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, 
+								default.phonelist, acoustic_model)
+					os.remove(label_file)
 
-                    prediction = am_func.read_fileFA(fa_file)
+					prediction = am_func.read_fileFA(fa_file)
 
-                    print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
-                else:
-                    prediction = ''
-                    print('!!!!! file not found.')
+					print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
+				else:
+					prediction = ''
+					print('!!!!! file not found.')
 
-                line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
-                predictions = predictions.append(line)
-            else:
-                prediction = ''
-                print('!!!!! invalid entry.')
+				line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
+				predictions = predictions.append(line)
+			else:
+				prediction = ''
+				print('!!!!! invalid entry.')
 
-        predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
+		predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
-## ======================= make files which is used for forced alignment by Kaldi =======================
-if make_kaldi_data_files:
 
-    wav_scp   = os.path.join(kaldi_data_dir, 'wav.scp')
-    text_file = os.path.join(kaldi_data_dir, 'text')
-    utt2spk   = os.path.join(kaldi_data_dir, 'utt2spk')
-
-    # remove previous files.
-    if os.path.exists(wav_scp):
-        os.remove(wav_scp)
-    if os.path.exists(text_file):
-        os.remove(text_file)
-    if os.path.exists(utt2spk):
-        os.remove(utt2spk)
-
-    f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
-    f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
-    f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
-
-    # make wav.scp, text, and utt2spk files.
-    for i in df.index:
-        filename = df['filename'][i]
-        print('=== {0}: {1} ==='.format(i, filename))
-
-        #if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
-        wav_file = os.path.join(wav_dir, filename)
-        if os.path.exists(wav_file):
-            speaker_id = 'speaker_' + str(i).zfill(4)
-            utterance_id = filename.replace('.wav', '')
-            utterance_id = utterance_id.replace(' ', '_')
-            utterance_id = speaker_id + '-' + utterance_id
-
-            # wav.scp file
-            wav_file_unix = wav_file.replace('\\', '/')
-            wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
-
-            f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
-
-            # text file
-            word = df['word'][i].lower()
-            f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
-
-            # utt2spk
-            f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
-
-    f_wav_scp.close()
-    f_text_file.close()
-    f_utt2spk.close()
 
 
 ## ======================= make lexicon txt which is used by Kaldi =======================
 if make_kaldi_lexicon_txt:
-    option_num = 6
+	option_num = 6
 
-    # remove previous file.
-    if os.path.exists(lexicon_txt):
-        os.remove(lexicon_txt)
-    lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
-    if os.path.exists(lexiconp_txt):
-        os.remove(lexiconp_txt)
+	# remove previous file.
+	if os.path.exists(lexicon_txt):
+		os.remove(lexicon_txt)
+	lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
+	if os.path.exists(lexiconp_txt):
+		os.remove(lexiconp_txt)
 				
-    # output lexicon.txt
-    f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
-    pronvar_list_all = []
-    for word in word_list:
+	# output lexicon.txt
+	f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
+	pronvar_list_all = []
+	for word in word_list:
 
-        # pronunciation variant of the target word.
-        pronunciation_variants = df['ipa'][df['word'].str.match(word)]
+		# pronunciation variant of the target word.
+		pronunciation_variants = df['ipa'][df['word'].str.match(word)]
 
-        c = Counter(pronunciation_variants)
-        total_num = sum(c.values())
+		c = Counter(pronunciation_variants)
+		total_num = sum(c.values())
 
-        #with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
-        #    for key in c.keys():
-        #        f.write("{0},{1}\n".format(key,c[key]))
+		#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
+		#    for key in c.keys():
+		#        f.write("{0},{1}\n".format(key,c[key]))
 
-        for key, value in c.most_common(option_num):
-            # make possible pronunciation variant list.
-            pronvar_list = am_func.fame_pronunciation_variant(key)
+		for key, value in c.most_common(option_num):
+			# make possible pronunciation variant list.
+			pronvar_list = am_func.fame_pronunciation_variant(key)
 
-            for pronvar_ in pronvar_list:
-                split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
-                pronvar_out = ' '.join(split_ipa) 
-                pronvar_list_all.append([word, pronvar_out])
+			for pronvar_ in pronvar_list:
+				split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
+				pronvar_out = ' '.join(split_ipa) 
+				pronvar_list_all.append([word, pronvar_out])
 
-    pronvar_list_all = np.array(pronvar_list_all)
-    pronvar_list_all = np.unique(pronvar_list_all, axis=0)
+	pronvar_list_all = np.array(pronvar_list_all)
+	pronvar_list_all = np.unique(pronvar_list_all, axis=0)
 
 	
-    # output
-    f_lexicon_txt.write('<UNK>\tSPN\n')
-    for line in pronvar_list_all:
-        f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
+	# output
+	f_lexicon_txt.write('<UNK>\tSPN\n')
+	for line in pronvar_list_all:
+		f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
 
-    f_lexicon_txt.close()
+	f_lexicon_txt.close()
 
 
 ## ======================= load kaldi forced alignment result =======================
 if load_forced_alignment_kaldi:
-    phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
-    merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
+	phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
+	merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
 	
-    #filenames	   = np.load(data_dir + '\\filenames.npy')
-    #words		   = np.load(data_dir + '\\words.npy')
-    #pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
-    #pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
-    #word_list = np.unique(words)    
+	#filenames	   = np.load(data_dir + '\\filenames.npy')
+	#words		   = np.load(data_dir + '\\words.npy')
+	#pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
+	#pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
+	#word_list = np.unique(words)    
 
-    # load the mapping between phones and ids.
-    with open(phones_txt, 'r', encoding="utf-8") as f:
-        mapping_phone2id = f.read().split('\n')
+	# load the mapping between phones and ids.
+	with open(phones_txt, 'r', encoding="utf-8") as f:
+		mapping_phone2id = f.read().split('\n')
 
-    phones = []
-    phone_ids = [] # ID of phones
-    for m in mapping_phone2id:
-        m = m.split(' ')
-        if len(m) > 1:
-            phones.append(m[0])
-            phone_ids.append(int(m[1]))
+	phones = []
+	phone_ids = [] # ID of phones
+	for m in mapping_phone2id:
+		m = m.split(' ')
+		if len(m) > 1:
+			phones.append(m[0])
+			phone_ids.append(int(m[1]))
 
 
-    # load the result of FA.
-    with open(merged_alignment_txt, 'r') as f:
-        lines = f.read()
-        lines = lines.split('\n')
+	# load the result of FA.
+	with open(merged_alignment_txt, 'r') as f:
+		lines = f.read()
+		lines = lines.split('\n')
 
-    predictions = pd.DataFrame({'filename': [''], 
-                'word': [''], 
-                'xsampa': [''],
-                'ipa': [''],
-                'famehtk': [''],
-                'prediction': ['']})
-    #fa_filenames = []
-    #fa_pronunciations = []
-    utterance_id_ = ''
-    pronunciation = []
-    for line in lines:
-        line = line.split(' ')
-        if len(line) == 5:
-            utterance_id = line[0]
-            if utterance_id == utterance_id_:
-                phone_id = int(line[4])
-                #if not phone_id == 1:
-                phone_ = phones[phone_ids.index(phone_id)]
-                phone  = re.sub(r'_[A-Z]', '', phone_)
-                if not phone == 'SIL':
-                    pronunciation.append(phone)
-            else:
-                filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
-                prediction = ''.join(pronunciation)
-                df_ = df[df['filename'].str.match(filename)]
-                df_idx = df_.index[0]
-                prediction_ = pd.Series([#filename,
-                                    #df_['word'][df_idx], 
-                                    #df_['xsampa'][df_idx], 
-                                    #df_['ipa'][df_idx], 
-                                    #df_['famehtk'][df_idx],
-                                    df_.iloc[0,1],
-                                    df_.iloc[0,3],
-                                    df_.iloc[0,4],
-                                    df_.iloc[0,2],
-                                    df_.iloc[0,0],
-                                    prediction], 
-                                    index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], 
-                                    name=df_idx)
-                predictions = predictions.append(prediction_)
-                #fa_filenames.append()
-                #fa_pronunciations.append(' '.join(pronunciation))
-                pronunciation = []
+	predictions = pd.DataFrame({'filename': [''], 
+				'word': [''], 
+				'xsampa': [''],
+				'ipa': [''],
+				'famehtk': [''],
+				'prediction': ['']})
+	#fa_filenames = []
+	#fa_pronunciations = []
+	utterance_id_ = ''
+	pronunciation = []
+	for line in lines:
+		line = line.split(' ')
+		if len(line) == 5:
+			utterance_id = line[0]
+			if utterance_id == utterance_id_:
+				phone_id = int(line[4])
+				#if not phone_id == 1:
+				phone_ = phones[phone_ids.index(phone_id)]
+				phone  = re.sub(r'_[A-Z]', '', phone_)
+				if not phone == 'SIL':
+					pronunciation.append(phone)
+			else:
+				filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
+				prediction = ''.join(pronunciation)
+				df_ = df[df['filename'].str.match(filename)]
+				df_idx = df_.index[0]
+				prediction_ = pd.Series([#filename,
+									#df_['word'][df_idx], 
+									#df_['xsampa'][df_idx], 
+									#df_['ipa'][df_idx], 
+									#df_['famehtk'][df_idx],
+									df_.iloc[0,1],
+									df_.iloc[0,3],
+									df_.iloc[0,4],
+									df_.iloc[0,2],
+									df_.iloc[0,0],
+									prediction], 
+									index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], 
+									name=df_idx)
+				predictions = predictions.append(prediction_)
+				#fa_filenames.append()
+				#fa_pronunciations.append(' '.join(pronunciation))
+				pronunciation = []
 
-            utterance_id_ = utterance_id
-    predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+			utterance_id_ = utterance_id
+	predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
 
 
 ## ======================= evaluate the result of forced alignment =======================
 if eval_forced_alignment_htk:
-    htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+	htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
 
-    compare_hmm_num = 1
+	compare_hmm_num = 1
 
-    if compare_hmm_num:
-        f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
-        f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
+	if compare_hmm_num:
+		f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+		f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
 
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-    #for hmm_num in [256]:
-        hmm_num_str = str(hmm_num)
-        if compare_hmm_num:
-            f_result.write("{},".format(hmm_num_str))
+	for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+	#for hmm_num in [256]:
+		hmm_num_str = str(hmm_num)
+		if compare_hmm_num:
+			f_result.write("{},".format(hmm_num_str))
 
-        #match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
-        #prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
-        #prediction = pd.Series(prediction, index=df.index, name='prediction')
-        #result = pd.concat([df, prediction], axis=1)
-        result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
+		#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+		#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
+		#prediction = pd.Series(prediction, index=df.index, name='prediction')
+		#result = pd.concat([df, prediction], axis=1)
+		result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
 
 
-        # load pronunciation variants
-        for word in word_list:
-            htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
-            with open(htk_dict_file, 'r') as f:
-                lines = f.read().split('\n')[:-1]
-                pronunciation_variants = [line.split('\t')[1] for line in lines]
+		# load pronunciation variants
+		for word in word_list:
+			htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+			with open(htk_dict_file, 'r') as f:
+				lines = f.read().split('\n')[:-1]
+				pronunciation_variants = [line.split('\t')[1] for line in lines]
 
-            # see only words which appears in top 3.
-            result_ = result[result['word'].str.match(word)]
-            result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
+			# see only words which appears in top 3.
+			result_ = result[result['word'].str.match(word)]
+			result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
 
-            match_num = sum(result_['famehtk'] == result_['prediction'])
-            total_num = len(result_)
+			match_num = sum(result_['famehtk'] == result_['prediction'])
+			total_num = len(result_)
 
-            print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
-            if compare_hmm_num:
-                f_result.write("{0},{1},".format(match_num, total_num))
-            else:
-                # output confusion matrix
-                cm = confusion_matrix(result_['famehtk'], result_['prediction'])
+			print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
+			if compare_hmm_num:
+				f_result.write("{0},{1},".format(match_num, total_num))
+			else:
+				# output confusion matrix
+				cm = confusion_matrix(result_['famehtk'], result_['prediction'])
 
-                plt.figure()
-                plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
-                plt.savefig(result_dir + '\\cm_' + word + '.png')
+				plt.figure()
+				plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+				plt.savefig(result_dir + '\\cm_' + word + '.png')
 
-        if compare_hmm_num:
-            f_result.write('\n')
+		if compare_hmm_num:
+			f_result.write('\n')
 
-    if compare_hmm_num:
-        f_result.close()
+	if compare_hmm_num:
+		f_result.close()
 
 
 ## ======================= evaluate the result of forced alignment of kaldi =======================
 if eval_forced_alignment_kaldi:
-    result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+	result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
 
-    f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
-    f_result.write("word,total,valid,match,[%]\n")
+	f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+	f_result.write("word,total,valid,match,[%]\n")
 
-    # load pronunciation variants
-    with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
-        lines = f.read().split('\n')[:-1]
-        pronunciation_variants_all = [line.split('\t') for line in lines]
+	# load pronunciation variants
+	with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
+		lines = f.read().split('\n')[:-1]
+		pronunciation_variants_all = [line.split('\t') for line in lines]
 
-    word_list = np.delete(word_list, [0], 0) # remove 'Oog'
-    for word in word_list:
+	word_list = np.delete(word_list, [0], 0) # remove 'Oog'
+	for word in word_list:
 
-        # load pronunciation variant of the word.
-        pronunciation_variants = []
-        for line in pronunciation_variants_all:
-            if line[0] == word.lower():
-                pronunciation_variants.append(line[1].replace(' ', ''))
+		# load pronunciation variant of the word.
+		pronunciation_variants = []
+		for line in pronunciation_variants_all:
+			if line[0] == word.lower():
+				pronunciation_variants.append(line[1].replace(' ', ''))
 
-        # see only words which appears in top 3.
-        result_ = result[result['word'].str.match(word)]        
-        result_tolerant = pd.DataFrame({
-            'filename': [''], 
-                'word': [''], 
-                'xsampa': [''],
-                'ipa': [''],
-                'prediction': [''],
-                'match': ['']})
+		# see only words which appears in top 3.
+		result_ = result[result['word'].str.match(word)]        
+		result_tolerant = pd.DataFrame({
+			'filename': [''], 
+				'word': [''], 
+				'xsampa': [''],
+				'ipa': [''],
+				'prediction': [''],
+				'match': ['']})
 
-        for i in range(0, len(result_)):
-            line = result_.iloc[i]
+		for i in range(0, len(result_)):
+			line = result_.iloc[i]
 
-            # make a list of all possible pronunciation variants of ipa description.
-            # i.e. possible answers from forced alignment.
-            ipa = line['ipa']
-            pronvar_list = [ipa]
-            pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
-            if not pronvar_list_ is None: 
-                pronvar_list += list(pronvar_list_)
+			# make a list of all possible pronunciation variants of ipa description.
+			# i.e. possible answers from forced alignment.
+			ipa = line['ipa']
+			pronvar_list = [ipa]
+			pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
+			if not pronvar_list_ is None: 
+				pronvar_list += list(pronvar_list_)
 
-            # only focus on pronunciations which can be estimated from ipa. 
-            if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
-                if line['prediction'] in pronvar_list:
-                    ismatch = True
-                else:
-                    ismatch = False
+			# only focus on pronunciations which can be estimated from ipa. 
+			if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
+				if line['prediction'] in pronvar_list:
+					ismatch = True
+				else:
+					ismatch = False
 
-                line_df = pd.DataFrame(result_.iloc[i]).T
-                df_idx = line_df.index[0]
-                result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
-                                              line_df.loc[df_idx, 'word'],
-                                              line_df.loc[df_idx, 'xsampa'],
-                                              line_df.loc[df_idx, 'ipa'],
-                                              line_df.loc[df_idx, 'prediction'],
-                                              ismatch], 
-                                        index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], 
-                                        name=df_idx)
-                result_tolerant = result_tolerant.append(result_tolerant_)
-        # remove the first entry (dummy)
-        result_tolerant = result_tolerant.drop(0, axis=0)
+				line_df = pd.DataFrame(result_.iloc[i]).T
+				df_idx = line_df.index[0]
+				result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
+											  line_df.loc[df_idx, 'word'],
+											  line_df.loc[df_idx, 'xsampa'],
+											  line_df.loc[df_idx, 'ipa'],
+											  line_df.loc[df_idx, 'prediction'],
+											  ismatch], 
+										index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], 
+										name=df_idx)
+				result_tolerant = result_tolerant.append(result_tolerant_)
+		# remove the first entry (dummy)
+		result_tolerant = result_tolerant.drop(0, axis=0)
 
-        total_num = len(result_)
-        valid_num = len(result_tolerant)
-        match_num = np.sum(result_tolerant['match'])
+		total_num = len(result_)
+		valid_num = len(result_tolerant)
+		match_num = np.sum(result_tolerant['match'])
 
-        print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
-        f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
+		print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
+		f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
 
-    f_result.close()
-        ## output confusion matrix
-        #cm = confusion_matrix(result_['ipa'], result_['prediction'])
+	f_result.close()
+		## output confusion matrix
+		#cm = confusion_matrix(result_['ipa'], result_['prediction'])
 
-        #plt.figure()
-        #plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
-        #plt.savefig(result_dir + '\\cm_' + word + '.png')
+		#plt.figure()
+		#plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+		#plt.savefig(result_dir + '\\cm_' + word + '.png')

acoustic_model/phoneset/fame_asr.py

@@ -68,14 +68,21 @@ phoneset = [
 # the phones which seldom occur are replaced with another more popular phones.
 # replacements are based on the advice from Martijn Wieling.
 reduction_key = {
-	'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g'
+	'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g',
+	# aki added because this is used in stimmen_project.
+	'ɔ̈:':'ɔ:'
 	}
 # already removed beforehand in phoneset. Just to be sure.
-phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
+phones_to_be_removed = ['ú', 's:']
 
 def phone_reduction(phones):
+	""" 
+	Args:
+		phones (list): list of phones.
+	"""
 	return [reduction_key.get(i, i) for i in phones
 				  if not i in phones_to_be_removed]
+
 phoneset_short = list(set(phone_reduction(phoneset)))
 phoneset_short.sort()
 
@@ -97,6 +104,7 @@ translation_key_asr2htk = {
 				
 	# refer to Xsampa. 
 	'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe', 
+	#'ɔ̈:': 'O:', # does not appear in FAME, but used in stimmen.
 	'ɛ': 'E', 'ɛ:': 'E:',
 	'ɪ': 'I', 'ɪ:': 'I:', 
 

acoustic_model/stimmen_functions.py

@@ -81,3 +81,25 @@ def add_row_asr(df):
 	for index, row in df.iterrows():
 		asr.append(fame_functions.ipa2asr(row['ipa']))
 	return df.assign(asr=asr)
+
+
+def load_pronunciations(WORD, htk_dic):
+	""" load pronunciation variants from HTK dic file. 
+
+	Args:
+		WORD (str): word in capital letters.
+		htk_dic (path): HTK dict file. 
+
+	Returns:
+		(pronunciations) (list): pronunciation variants of WORD.
+		
+	Notes:
+		Because this function loads all contents from htk_dic file, 
+		it is not recommended to use for large lexicon. 
+
+	"""
+	with open(htk_dic) as f:
+		lines = f.read().replace(' sil', '')
+		lines = lines.split('\n')
+	return [' '.join(line.split(' ')[1:]) 
+		 for line in lines if line.split(' ')[0]==WORD]

acoustic_model/stimmen_test.py

@@ -2,8 +2,9 @@ import os
 os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 import sys
 import shutil
+from collections import Counter
 
-#import numpy as np
+import numpy as np
 import pandas as pd
 
 import defaultfiles as default
@@ -62,3 +63,18 @@ for ipa in df['ipa']:
 	if ':' in ipa_splitted:
 		print(ipa_splitted)
 
+
+## check pronunciation variants
+df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
+df_clean = stimmen_functions.add_row_asr(df_clean)
+df_clean = stimmen_functions.add_row_htk(df_clean)
+
+for word in word_list:
+#word = word_list[1]
+	df_ = df_clean[df_clean['word']==word]
+	c = Counter(df_['htk'])
+	pronunciations = dict()
+	for key, value in zip(c.keys(), c.values()):
+		if value > 3:
+			pronunciations[key] = value
+	print(pronunciations)